## DTQ doses in data:
##
## 0 50 100 150 300 600
## 186 55 57 1 481 57
##
## Brazil Cambodia Colombia Ethiopia India Peru
## 233 29 13 42 51 298
## Philippines Thailand Vietnam
## 4 138 29
## [1] 0.5476451 14.2857141
## Median number of methb measurements is 11
## [1] 2 17
##
## 0 1
## 543 294
## Dose (mg) Rec 6 mths (%) n
## 1 0 60.8 186.0
## 2 50 40.0 55.0
## 3 100 42.1 57.0
## 4 150 0.0 1.0
## 5 300 27.0 481.0
## 6 600 8.8 57.0
CYP2D6
##
## 0 0.5 1 1.5 2
## *1/*1 0 0 0 0 306
## *1/*10 0 0 0 37 0
## *1/*17 0 0 0 25 0
## *1/*3 0 0 3 0 0
## *1/*4 0 0 46 0 0
## *1/*41 0 0 0 19 0
## *1/*5 0 0 12 0 0
## *1/*6 0 0 1 0 0
## *1/*9 0 0 0 9 0
## *10/*10 0 0 26 0 0
## *10/*41 0 0 6 0 0
## *17/*17 0 0 3 0 0
## *17/*41 0 0 2 0 0
## *3/*4 1 0 0 0 0
## *3/*9 0 1 0 0 0
## *4/*17 0 1 0 0 0
## *4/*4 2 0 0 0 0
## *4/*41 0 6 0 0 0
## *4/*5 2 0 0 0 0
## *4/*9 0 1 0 0 0
## *41/*41 0 0 2 0 0
## *5/*10 0 7 0 0 0
## *5/*41 0 1 0 0 0
## *5/*5 1 0 0 0 0
## *5/*9 0 1 0 0 0
##
## *1 *10 *17 *3 *4 *41 *5 *6 *9
## 73.3 9.8 3.3 0.5 5.9 3.6 2.4 0.1 1.2
##
## 0 0.5 1 1.5 2
## 0 5 0 0 0 0
## 0.25 0 7 0 0 0
## 0.5 1 10 26 0 0
## 0.75 0 0 6 0 0
## 1 0 1 65 0 0
## 1.25 0 0 0 37 0
## 1.5 0 0 4 53 0
## 2 0 0 0 0 306
## Activity Score
## CYP2D6 Genotype 0 0.25 0.5 0.75 1 1.25 1.5 2
## *1/*1 0 0 0 0 0 0 0 306
## *1/*10 0 0 0 0 0 37 0 0
## *1/*17 0 0 0 0 0 0 25 0
## *1/*3 0 0 0 0 0 0 3 0
## *1/*4 0 0 0 0 46 0 0 0
## *1/*41 0 0 0 0 0 0 19 0
## *1/*5 0 0 0 0 12 0 0 0
## *1/*6 0 0 0 0 0 0 1 0
## *1/*9 0 0 0 0 0 0 9 0
## *10/*10 0 0 26 0 0 0 0 0
## *10/*41 0 0 0 6 0 0 0 0
## *17/*17 0 0 0 0 3 0 0 0
## *17/*41 0 0 0 0 2 0 0 0
## *3/*4 0 0 1 0 0 0 0 0
## *3/*9 0 0 0 0 1 0 0 0
## *4/*17 0 0 1 0 0 0 0 0
## *4/*4 2 0 0 0 0 0 0 0
## *4/*41 0 0 6 0 0 0 0 0
## *4/*5 2 0 0 0 0 0 0 0
## *4/*9 0 0 1 0 0 0 0 0
## *41/*41 0 0 0 0 2 0 0 0
## *5/*10 0 7 0 0 0 0 0 0
## *5/*41 0 0 1 0 0 0 0 0
## *5/*5 1 0 0 0 0 0 0 0
## *5/*9 0 0 1 0 0 0 0 0
weight
## country outcome7to180.1 outcome7to180.2
## 1 Brazil 32 155
## 2 Cambodia 58 19
## 3 Colombia 31 13
## 4 Ethiopia 21 28
## 5 India 0 9
## 6 Peru 28 167
## 7 Philippines 0 2
## 8 Thailand 15 59
## 9 Vietnam 14 29
## country outcome7to180.1 outcome7to180.2
## 1 Brazil 42 233
## 2 Cambodia 69 29
## 3 Colombia 31 13
## 4 Ethiopia 33 42
## 5 India 4 51
## 6 Peru 39 298
## 7 Philippines 25 4
## 8 Thailand 25 138
## 9 Vietnam 14 29
##
## 0 50 100 150 300 600
## 186 55 57 1 481 57
## [1] 481
##
## Call:
## lm(formula = day7_mthb ~ tqmgkgtot + AUC + (AS_score < 1), data = outcome_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2706 -1.0008 -0.3138 0.6156 5.3151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.523853 0.390859 -1.340 0.1810
## tqmgkgtot 0.530779 0.071620 7.411 9.11e-13 ***
## AUC 0.001372 0.001992 0.689 0.4914
## AS_score < 1TRUE -0.495926 0.257754 -1.924 0.0551 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.36 on 358 degrees of freedom
## (475 observations deleted due to missingness)
## Multiple R-squared: 0.1436, Adjusted R-squared: 0.1365
## F-statistic: 20.02 on 3 and 358 DF, p-value: 5.133e-12
##
## Call:
## lm(formula = log10(day7_mthb) ~ tqmgkgtot + t_12_terminal, data = outcome_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.28370 -0.17867 0.00235 0.18818 0.69908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.269373 0.078194 3.445 0.000613 ***
## tqmgkgtot 0.059148 0.005156 11.473 < 2e-16 ***
## t_12_terminal -0.019105 0.003880 -4.924 1.11e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.268 on 571 degrees of freedom
## (263 observations deleted due to missingness)
## Multiple R-squared: 0.2494, Adjusted R-squared: 0.2467
## F-statistic: 94.84 on 2 and 571 DF, p-value: < 2.2e-16
## [1] 17.44966
##
## Call:
## lm(formula = t_12_terminal ~ AS_score <= 0.5, data = outcome_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9907 -2.0747 -0.3297 2.0003 9.9986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.8721 0.1602 111.569 <2e-16 ***
## AS_score <= 0.5TRUE -1.0562 0.5404 -1.954 0.0514 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.01 on 385 degrees of freedom
## (450 observations deleted due to missingness)
## Multiple R-squared: 0.009824, Adjusted R-squared: 0.007252
## F-statistic: 3.82 on 1 and 385 DF, p-value: 0.05138
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0766413 0.56145372 -1.917596 5.516228e-02
## tqmgkgtot -0.3167101 0.03578999 -8.849125 8.820840e-19
## logpara0 0.3393301 0.14151979 2.397757 1.649580e-02
## Using all data (n=836), the odds ratio for recurrence at 6 months for each additional mg/kg of tafenoquine is 0.73 (95% CI 0.68 to 0.78)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3024656 0.8553941 0.3535979 0.723640219
## tqmgkgtot -0.3718242 0.1158668 -3.2090673 0.001331663
## logpara0 0.1189197 0.1799389 0.6608894 0.508683256
## Using only patients who got a 300 mg dose (n=481), the odds ratio for recurrence at 6 months for each additional mg/kg of tafenoquine is 0.69 (95% CI 0.55 to 0.87)
##
## Family: binomial
## Link function: logit
##
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 5) + logpara0 + s(studysite,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2837 0.5979 -3.820 0.000134 ***
## logpara0 0.3403 0.1423 2.391 0.016807 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(tqmgkgtot) 1.00 1.001 78.84 < 2e-16 ***
## s(studysite) 16.48 21.000 54.20 6.95e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.181 Deviance explained = 16.7%
## UBRE = 0.12654 Scale est. = 1 n = 836
##
## Family: binomial
## Link function: logit
##
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 5) + logpara0 + s(studysite,
## bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6467 0.6851 -2.404 0.0162 *
## logpara0 0.1355 0.1830 0.740 0.4591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(tqmgkgtot) 1.710 2.137 9.553 0.00895 **
## s(studysite) 6.858 20.000 10.571 0.08467 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0403 Deviance explained = 5.44%
## UBRE = 0.14752 Scale est. = 1 n = 481
Figure 1 of results in paper: the main mg/kg driving efficacy plot
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot =
## xs_all, : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_sens_gam, newdata = data.frame(tqmgkgtot =
## xs_sens, : factor levels -1 not in original fit
compare logistic regression fits and spline fits
Compare for different parts of the world
##
## Africa Americas Asia-Pacific
## 42 544 251
## In Americas the odds ratio for recurrence for each additional mg/kg increase in tafenoquine dose is 0.73
##
## Family: binomial
## Link function: logit
##
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 3) + s(studysite, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.46618 0.09472 -4.922 8.58e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(tqmgkgtot) 1.000e+00 1 60.85 <2e-16 ***
## s(studysite) 4.724e-05 8 0.00 0.983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.125 Deviance explained = 9.73%
## UBRE = 0.22299 Scale est. = 1 n = 544
## Warning in predict.gam(mod_rg, newdata = data.frame(tqmgkgtot = xs_all, : factor
## levels -1 not in original fit
## In Asia-Pacific the odds ratio for recurrence for each additional mg/kg increase in tafenoquine dose is 0.68
##
## Family: binomial
## Link function: logit
##
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 3) + s(studysite, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6509 0.9704 -2.732 0.0063 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(tqmgkgtot) 1.708 1.914 18.90 0.000962 ***
## s(studysite) 8.318 10.000 38.34 1.56e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.292 Deviance explained = 30.8%
## UBRE = -0.1382 Scale est. = 1 n = 251
## Warning in predict.gam(mod_rg, newdata = data.frame(tqmgkgtot = xs_all, : factor
## levels -1 not in original fit
## In Africa the odds ratio for recurrence for each additional mg/kg increase in tafenoquine dose is 0.73
##
## Family: binomial
## Link function: logit
##
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 3) + s(studysite, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7729 0.4451 -1.736 0.0825 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(tqmgkgtot) 1.0000 1 5.543 0.0186 *
## s(studysite) 0.3487 1 0.531 0.2174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.132 Deviance explained = 12.6%
## UBRE = 0.22433 Scale est. = 1 n = 42
## Warning in predict.gam(mod_rg, newdata = data.frame(tqmgkgtot = xs_all, : factor
## levels -1 not in original fit
## In patients who recived 300 mg single dose:
## region outcome7to180
## 1 Africa 21.4
## 2 Americas 29.9
## 3 Asia-Pacific 20.3
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.0929008 0.89121117 -4.592515 4.379357e-06
## t_12_terminal 0.1322341 0.03232949 4.090200 4.310022e-05
## logpara0 0.1725178 0.16345216 1.055463 2.912135e-01
## Using all data (n=836), the odds ratio for recurrence at 6 months for each additional mg/kg of tafenoquine is 0.73 (95% CI 0.68 to 0.78)
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot =
## sim_dose_mgkg_current, : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot =
## sim_dose_mgkg_revised, : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot = 450/
## sim_ws[sim_ws >= : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot = 600/
## sim_ws[sim_ws >= : factor levels -1 not in original fit
## Under the current dosing the mean recurrence proportion within 6 months is 21.4%
## Under the revised dosing the mean recurrence proportion within 6 months is 7.9%
## If all adults>45 kg get 450 mg the mean recurrence proportion within 6 months is 11.9%
## If all adults>45 kg get 600 mg the mean recurrence proportion within 6 months is 6.2%
Superiority of 450 over 300 mg single dose